A variety of different applications use sensor systems to detect the movement of an underlying object. For example, inertial sensors, e.g., accelerometers or gyroscopes, are used in safety and navigation systems for automotive, military, aerospace and marine applications. In automotive safety systems, inertial sensors are often mounted about the periphery of an automobile chassis to sense pre-specified accelerations or rotations. The sensors typically cooperate with a central computer that both coordinates their function and responds to pre-specified types of detected movement.
Upon detection of a pre-specified type of movement, the sensors transmit motion data to the computer, which causes systems within the automobile to respond in a pre-specified manner. For example, if the sensors detect a sudden and high deceleration, air-bag systems may deploy their air bags. Alternatively, if the sensors detect a sudden rotation, e.g., the automobile is swerving, breaking systems may selectively break to avoid a rollover. Accordingly, sensors have become critical in safety systems.
To operate properly, sensor systems require accurate calibration of the sensor null output, i.e., the sensor reading when the sensor is not moving. Specifically, sensor systems typically use the sensor null output value as an offset value to the sensor reading to accurately determine the environmental parameter being measured, e.g., angular movement. It is desirable to implement safety systems with affordable sensors so that safety improvements may be made more widely available. However, low cost sensors often have undesirable long-term drift of the sensor null value, which can invalidate the factory calibration settings for the sensor. In other words, long-term drift can corrupt the ultimate readings of the sensor, making the sensor less accurate over time.